人工智能是一種“全新生產要素”

Talk to a bunch of economists and they will doubtless tell you that poor productivity growth is the scourge of our age.

與一些經濟學家交談，他們幾乎肯定會告訴你，疲弱的生產率增長是我們這個時代的災難。

Lounge in the back of a limo with some chief executives, on the other hand, and they will enthuse about how new technologies are transforming corporate productivity.

另一方面，舒服地靠在一些首席執行官的豪華轎車的後座上，他們會熱情洋溢地訴說新技術正如何改變企業生產率。

Track down some experts in artificial intelligence and they may well babble on about standing on the brink of a productivity revolution. If we ever reach the point of technological singularity — when computers outsmart humans — productivity growth will accelerate exponentially.

與人工智能領域的一些專家談話，他們很有可能會喋喋不休地說着我們正瀕臨一場生產率革命。如果我們達到技術奇點（當電腦智慧超過人類智慧時），生產率增速將呈指數式加快。

From that moment, a computer superintelligence will rapidly discover everything left to discover. This Master Algorithm, as the author — a computer science professor at the University of Washington — Pedro Domingos calls it, will be the last invention that man makes. It will be able to derive all knowledge in the world — past, present, and future — from data.

從那一刻起，電腦超級智能將迅速發現留待發現的一切。正如華盛頓大學(University of Washington)計算機學教授、《主算法》(Master Algorithm)一書作者佩德羅?多明戈斯(Pedro Domingos)所說，這個主算法將成爲人類的最後一個發明。這個主算法將能夠從數據中獲得世界上的一切知識——過去、現在和未來。

There does appear to be, to put it mildly, something of a “productivity paradox”. Can all three stories be true? Quite possibly, yes.

說得婉轉些，其中似乎確實存在某種“生產率悖論”。這3個故事有可能全部爲真嗎？很有可能，是的。

Hype, of course, is not an alien phenomenon in the tech industry. At present, we are a very, very long way from technological singularity and opinion is divided about whether we will ever reach it. It is worth noting, though, that some (younger) researchers in the field are convinced they will achieve it in their lifetimes.

當然，在科技行業，天花亂墜的宣傳並不新鮮。目前，我們距離技術奇點還相當遙遠，關於我們達到這個奇點的那一天會不會到來，人們還沒有達成一致。然而，我們有必要注意到，該領域有些（較年輕）的研究人員相信，他們將在他們的有生之年迎來這一刻。

Yet even the application of narrow, domain-specific AI that exists today is producing startling results as the big tech companies — Google, Microsoft and IBM — pour money into the field. For a glimpse of what is possible, it is worth checking in with BenevolentAI, a London start-up attempting to revolutionise medical research.

然而，即便是目前存在的狹窄、針對特定領域的人工智能應用也在產生驚人的結果——大型科技公司（谷歌(Google)、微軟(Microsoft)和IBM）正在該領域投入資金。要了解未來可能發生的事情，我們有必要關注一下倫敦初創企業BenevolentAI，該公司試圖實現醫學研究的革命。

Kenneth Mulvany, Benevolent’s founder, argues that drug discovery is in large part an information and data challenge that can be effectively addressed by AI. PubMed, the online medical research site, holds 26m citations and is adding about 1m new publications a year. That is clearly more than any team of researchers could ingest in a lifetime.

BenevolentAI創始人肯尼思?梅爾文(Kenneth Mulvany)認爲，藥品的發現在很大程度上是一項信息和數據挑戰，這些挑戰能夠由人工智能有效解決。在線醫學研究網站PubMed擁有2600萬篇文獻，並每年新增約100萬篇文獻。這顯然是任何一個研究團隊所有成員一輩子都無法完全吸收的。

Benevolent has built a computer “engine” capable of reading and mapping such data and extracting relevant information, highlighting “conceptual hypotheses” in one field that can be applied to another. “You can look at things on a scale that was unimaginable before,” Mr Mulvany says. “This AI-assessed component can augment human intelligence.”

BenevolentAI搭建了一個電腦“引擎”，能夠閱讀這些數據、對其整理歸類並提取相關信息，突出顯示一個領域中能夠應用於另一個領域的“概念假說”。“你可以用以前想象不到的規模來看事情，”馬爾瓦尼表示，“這種由人工智能評估的組件可以增強人類智慧。”

Benevolent is working with researchers at Sheffield university to investigate new pathways to treat motor neurone disease and amyotrophic lateral sclerosis (ALS). Early results are promising.

BenevolentAI正與謝菲爾德大學(Sheffield university)的研究人員合作，以研究治療運動神經元疾病和肌萎縮性側索硬化症(ALS)的新方法。初步結果大有希望。

Richard Mead, lecturer in neuroscience, says that Benevolent has already validated one pathway for drug discovery and opened up a surprising new one. “What their engine can do is look across vast swaths of information to pick novel ideas to repurpose.”

神經學講師理查德?米德(Richard Mead)表示，BenevolentAI已確認一種藥物發現的途徑並開啓了一種驚人的新途徑。“他們的引擎可以瀏覽大量信息，以發現新的想法重新利用。”

It can also help personalise solutions for individuals according to their genetic make up. “We are really excited about it. The potential is incredible,” says Laura Ferraiuolo, lecturer in translational neurobiology.

它還可以幫助根據基因構成來制定個性化的個人解決方案。轉化神經生物學講師勞拉?費拉約洛(Laura Ferraiuolo)表示：“我們確實對此感到興奮。潛力是驚人的。”

Some economists argue this combination of fast-expanding data sets, machine learning and ever-increasing computing power should be classified as an entirely new factor of production, alongside capital and labour.

一些經濟學家認爲，迅速擴大的數據集、機器學習和日益提高的計算能力，這些都應被列爲除資本和勞動力之外的一種全新的生產要素。

AI is creating a new “virtual workforce”, enhancing the productivity of human intelligence and driving new innovation. Moreover, unlike other factors of production, AI does not degrade over time. Rather, it benefits from network and scale effects. Every self-driving car can “learn” from every other such vehicle, for example.

人工智能正締造一種新的“虛擬勞動力”，提高人類智慧的生產率並推動新的創新。另外，與其他生產要素不同，人工智能不會隨着時間的流逝而貶值。它將受益於網絡和規模效應。例如所有自動駕駛汽車都能從其他此類汽車身上學習。

A recent report from Accenture and Frontier Economics made the bold claim that the widespread adoption of AI-enabled technologies could double the economic growth rates of many advanced countries by 2035.

來自埃森哲(Accenture)與經濟學前沿公司(Frontier Economics)最近的一份報告大膽提出，到2035年，基於人工智能的技術的普遍採用，可能會將很多發達國家的經濟增速提高一倍。

It estimated that AI had the potential to raise the annual growth rate of gross value added (a close approximation of GDP) to 4.6 per cent in the US, 3.9 per cent in the UK and 2.7 per cent in Japan.

報告估計，人工智能有可能將美國、英國和日本的總增加值（與國內生產總值(GDP)近似）年度增速分別提高到4.6%、3.9%和2.7%。

Such studies are educated guesswork. Advances in technology are unpredictable. But some AI pioneers are convinced it could “change everything”, from material science to energy. “We are at the dawn of a new age of innovation,” says Mr Mulvany. “We already have human-augmented innovation. We will eventually have machine innovation.”

這些研究屬於學術猜測。科技的進步是不可預測的。但一些人工智能先驅相信，它可以“改變一切”，從材料科學到能源。“我們正處在一個新的創新時代的開端，”馬爾瓦尼表示，“我們已擁有由人類增強的創新。我們將最終擁有機器創新。”

Even the most gimlet-eyed of economists may soon have to accept that AI is affecting productivity in profound and possibly extraordinary ways.

甚至連目光最犀利的經濟學家可能也很快不得不承認，人工智能將以深遠且可能非同一般的方式影響生產率。